title: SEMG pattern classification using hierarchical Bayesian model.
abstract: This work addresses surface electromyogram (sEMG)-based muscle pattern classification using a generative model. By using a hierarchical Bayesian model, the proposed approach constructs an overall process model of recorded sEMG signals. By inferring probabilistically latent neural states which governs a collection of training sEMG data, classification is realized. To validate the approach, eight-class classification using four sEMG sensors on the limb actions is tested with five subjects. The proposed model achieves an overall 95% accuracy in the classification experiment. The results support that the proposed approach is very promising for sEMG pattern classification.
authors.forename: Hyonyoung
authors.initials: H
authors.lastname: Han
authors.forename: Sungho
authors.initials: S
authors.lastname: Jo
mesh_terms: D001499:Bayes Theorem; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D034941:Upper Extremity / Q000502:physiology*